We introduced and discussed an implementation in below post.
The implementation used in above post uses an array of alphabet size with every node. It can be made memory efficient. One way to implementing Trie is linked set of nodes, where each node contains an array of child pointers, one for each symbol in the alphabet. This is not efficient in terms of time as we can’t quickly find a particular child.
The efficient way is an implementation where we use hash map to store children of a node. Now we allocate memory only for alphabets in use, and don’t waste space storing null pointers.
1 1 0 1 1
Space used here with every node here is proportional to number of children which is much better than proportional to alphabet size, especially if alphabet is large.
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- Trie | (Delete)
- Boggle | Set 2 (Using Trie)
- Trie | (Display Content)
- Implement a Dictionary using Trie
- Insertion in a Trie recursively
- Bottom-up traversal of a Trie
- Trie | (Insert and Search)
- Search in a trie Recursively
- Persistent Trie | Set 1 (Introduction)
- Longest Common Prefix using Trie
- Program for assigning usernames using Trie
- Pattern Searching using a Trie of all Suffixes
- Auto-complete feature using Trie
- Count inversions in an array | Set 4 ( Using Trie )
- Advantages of Trie Data Structure
- Counting the number of words in a Trie
- Check if the given Trie contains words starting from every alphabet
- Count the number of words with given prefix using Trie
- Print all possible combinations of words from Dictionary using Trie
- Trie Data Structure using smart pointer and OOP in C++